The field of the invention is computer methods for analyzing nonlinear processes to forewarn of critical events in nonlinear processes
Examples of critical events are mechanical or electrical failures in machines; epileptic seizures, ventricular fibrillations, fainting (syncope), breathing difficulty, and sepsis in human medical patients; and other physical processes. Further examples of nonlinear processes include brain waves, heart waves, chest sounds, transients in power systems, airflow over automobiles and airplanes, weather and climate dynamics, water flow around submarines, machine tool-part interaction (e.g., tool chatter), nuclear reactor instabilities, fusion plasma instabilities, earthquakes, turbulent flow in pipes, planetary/satellite motion.
Engineering, medical, and research applications frequently must distinguish a difference between two apparently similar but actually different states in a nonlinear process. Examples include various data for machine failure, pre-seizure versus non-seizure brain waves, pre-fibrillation versus fibrillation heart waves, pre-syncope versus syncope heart waves, pre-sepsis versus sepsis heart waves, and normal versus abnormal chest sounds as an indicator of breathing difficulty. The electrical/mechanical community calls this problem, “condition monitoring.” The equivalent term in the medical community is diagnostic, medical, or health monitoring. In the computer/networking world, the term is “security monitoring.”
Hively et al., U.S. Pat. Nos. 5,743,860 and 5,857,978 disclose methods for detecting and predicting epileptic seizures by acquiring brain wave data from a patient, and analyzing the data with traditional nonlinear methods.
Hively et al., U.S. Pat. No. 5,815,413 discloses the applicability of nonlinear techniques to monitor machine conditions, such as the condition of a drill bit or the performance of an electrical motor driving a pump.
Clapp et al., U.S. Pat. No. 5,626,145 discloses the removal of artifacts representing eye blinks from EEG data using a zero-phase quadratic filter. As one normally skilled in the art can appreciate, the same method can remove other artifacts from data, such as quasi-periodic variations from three-phase electrical current, voltage, or power; and quasi-periodic oscillations from one or more channels of acceleration; and breathing artifacts from heart waves and chest sounds;.
Hively et al., U.S. Pat. No. 6,484,132 discloses a plurality of nonlinear techniques for using and enhancing phase space dissimilarity measures (PSDM) to forewarn of machine failures, as well as impending epileptic events from scalp EEG in ambulatory settings. PSDM yield superior performance over traditional nonlinear indicators, such as Kolmogorov entropy, Lyapunov exponents, and correlation dimension.